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It is known that artificial intelligence algorithms are based on calculations performed using various mathematical operations. In order for these calculation processes to be carried out correctly, some types of data cannot be fed directly into the algorithms. In other words, numerical data should be input to these algorithms, but not all data in datasets collected for artificial intelligence algorithms are always numerical. These data may not be quantitative but may be important for the study under consideration. That is, these data cannot be thrown away. In such a case, it is necessary to transfer categorical data to numeric type. In this research work, 14 encoding methods of transforming of categorical data were considered. At the same time, conclusions are given about the general conditions of using these methods. During the research, categorical data in the dataset that were collected in order to assess whether it is possible to give credit to customers will be transformed based on 14 methods. After applying each encoding method, experimental tests are conducted based on the classification algorithm, and they are evaluated. At the end of the study, the results of the experimental tests are discussed and research conclusions are presented. [ABSTRACT FROM AUTHOR]

This study describes our thorough experience of formal modeling and exhaustive verification of concurrent systems, particularly mutual exclusion algorithms. The experience focuses on Lycklama and Hadzilacos's (LH) mutual exclusion algorithm. LH rests on the reduced size of the shared state, contains a mechanism that tries to enforce an FCFS order to processes entering their critical section, and embodies Burns and Lamport's (BL) mutual exclusion algorithm. The modeling methodology is based on timed automata and the model checker of the popular Uppaal toolbox. The effectiveness of the modeling and analysis approach is first demonstrated by studying the BL's solution and retrieving all its properties, including, in general, its unbounded overtaking, which is the non-limited number of by-passes a process can suffer before accessing its critical section. Then, the LH algorithm is investigated in depth by showing it fulfills all the mutual exclusion properties when it operates with atomic memory. However, as this study demonstrates, LH is not free of deadlocks when used with non-atomic memory. Finally, a state-of-the-art mutual exclusion solution is proposed, which relies on a stripped-down LH version for processes, which is used as the arbitration unit in a tournament tree (TT) organization. This study documents that LH's TT-based algorithm satisfies all the mutual exclusion properties, with a linear overtaking, both using atomic and non-atomic memory. [ABSTRACT FROM AUTHOR]

Data discretisation focuses on converting continuous attribute values to discrete ones which are closer to a knowledge-level representation that is easier to understand, use, and explain than continuous values. On the other hand, instance selection aims at filtering out noisy or unrepresentative data samples from a given training dataset before constructing a learning model. In practice, some domain datasets may require processing with both discretisation and instance selection at the same time. In such cases, the order in which discretisation and instance selection are combined will result in differences in the processed datasets. For example, discretisation can be performed first based on the original dataset, after which the instance selection algorithm is used to evaluate the discrete type of data for selection, whereas the alternative is to perform instance selection first based on the continuous type of data, then using the discretiser to transfer the attribute type of values of a reduced dataset. However, this issue has not been investigated before. The aim of this paper is to compare the performance of a classifier trained and tested over datasets processed by these combination orders. Specifically, the minimum description length principle (MDLP) and ChiMerge are used for discretisation, and IB3, DROP3 and GA for instance selection. The experimental results obtained using ten different domain datasets show that executing instance selection first and discretisation second perform the best, which can be used as the guideline for the datasets that require performing both steps. In particular, combining DROP3 and MDLP can provide classification accuracy of 0.85 and AUC of 0.8, which can be regarded as the representative baseline for future related researches. [ABSTRACT FROM AUTHOR]

The article presents a mathematical model and algorithm for optimizing the picking process in a warehouse. The algorithm aims to minimize transportation costs and maximize efficiency by determining which shelves should be transported to the picking station. It uses a hybrid intelligent approach, combining a heuristic algorithm and a genetic algorithm. The algorithm is validated through simulations and found to outperform other algorithms. The article concludes that the proposed algorithm improves picking efficiency and can be applied in various contexts. [Extracted from the article]

Route planning for automated guided vehicles (AGVs) is one of the key factors that affects work efficiency of automated storage and retrieval systems (AS/RSes). Route planning plays an important role in the operation of AGVs. Since the characteristic of AS/RSes is chessboard-like, the environment is more complex than traditional route planning environments because the number of nodes is large, more than one shortest route exists between two nodes, and the routes with the shortest distance may not be the most energy-saving routes. Although the traditional route planning algorithms such as the classical Q-learning algorithm can work well in AGV route planning, it also has some limitations. This paper proposes a novel multi-AGV route planning approach to solving the AGV route planning problem in the chessboard-like warehouse, which can improve the route planning efficiency greatly. First, by combining adjacency matrix and reward matrix, we propose a low-dimensional adjacency-reward matrix for route planning. This algorithm improves the efficiency of classical Q-learning algorithms and accelerates dynamic route planning significantly. We further improve the algorithm by considering the travel directions to minimize the number of turns in the route and additionally by considering whether the AGV is loaded or not and plan routes accordingly. Finally, we propose a multi-AGV online collision-free route planning algorithm based on these considerations for dynamic route planning for multi-AGVs operating in a large-scale warehouse. The proposed algorithms are validated with several case studies. [ABSTRACT FROM AUTHOR]

The effective distribution of relief goods is critical in mitigating the impact of natural disasters and preserving human life. This study addresses a relief goods distribution problem, assuming the existence of multiple relief orders that must be delivered to various disaster-stricken regions from a network of warehouses using a fleet of diverse vehicles. The objective is to identify the most suitable warehouse for each relief order, allocate relief orders to vehicles, batch the orders in the designated vehicles, and devise routing plans to minimize the total delivery time. A mixed-integer linear programming model is formulated to tackle this problem. Owing to the problem's NP-hard nature, a metaheuristic algorithm, known as the Multiple League Championship Algorithm, is developed. Furthermore, two innovative variants of the MLCA, namely the League Base Multiple League Championship Algorithm (L-MLCA) and the Playoff Multiple League Championship Algorithm (P-MLCA), are introduced. Experimental results indicate that the P-MLCA outperforms the other two algorithms. The solutions derived from the P-MLCA are compared with the optimal solutions obtained by a commercial solver for small-scale problems. This comparative analysis demonstrates the promising performance of the P-MLCA in finding the optimal distribution of relief goods. [ABSTRACT FROM AUTHOR]

Litchi is a highly favored fruit with high economic value. Mechanical automation of litchi picking is a key link for improving the quality and efficiency of litchi harvesting. Our research team has been conducting experiments to develop a visual-based litchi picking robot. However, in the early physical prototype experiments, we found that, although picking points were successfully located, litchi picking failed due to random obstructions of the picking points. In this study, the physical prototype of the litchi picking robot previously developed by our research team was upgraded by integrating a visual system for actively removing obstructions. A framework for an artificial intelligence algorithm was proposed for a robot vision system to locate picking points and to identify obstruction situations at picking points. An intelligent control algorithm was developed to control the obstruction removal device to implement obstruction removal operations by combining with the obstruction situation at the picking point. Based on the spatial redundancy of a picking point and the obstruction, the feeding posture of the robot was determined. The experiment showed that the precision of segmenting litchi fruits and branches was 88.1%, the recognition success rate of picking point recognition was 88%, the average error of picking point localization was 2.8511 mm, and an overall success rate of end-effector feeding was 81.3%. These results showed that the developed litchi picking robot could effectively implement obstruction removal. [ABSTRACT FROM AUTHOR]

The aim of this work is to obtain multi-objective linear programming algorithms that can be used to solve the global problem of multi-object safety control processes in order to minimize the risk of collisions. In multi-objective linear optimization models, satisfactory trade-off assesses and resolves the conflict between different control objectives. A comparison of single-, bi-, and tri-objective linear programming algorithms allows us to adapt the appropriate optimization method to the conditions of the control process. An important outcome of the present research is the demonstration of the greater effectiveness of bi- and tri-objective optimization compared to single-objective optimization, reflecting the compromises taken into account when choosing between objects and achieving a minimum risk of collision when passing them. [ABSTRACT FROM AUTHOR]

With the rapid development of e-commerce, the orders processed in B2C warehouses are characterised by heterogeneous and small volume. The traditional storage assignment strategies used in the picker-to-parts warehouses do not have advantage any more. In this case, the scattered storage strategy is a good alternative. In this paper, we study a new scattered storage strategy that allows the same product to be placed in multiple storage locations. The correlation between products which reflects how frequently any two products will be ordered together in the same order is considered. The problem is formulated as a 0-1 integer programming model to minimise the weighted sum of distances between the products, with weight being the elements of the correlation matrix. To solve large-scale problems, a GA and a basic PSO algorithm are developed. To improve solution quality, a new PSO algorithm based on the problem characteristic is designed and a hybrid algorithm combing it with GA is proposed. Experiments show that the solutions of these algorithms are close to the optimal solutions for the small-sale problems. For larger problems, the specially designed new PSO greatly improves solution quality as compared to the basic algorithms and the hybrid algorithm makes further improvement. [ABSTRACT FROM AUTHOR]

Decisions made for designing and operating a warehouse system are of great significance. These operational decisions are strongly affected by total logistics costs, including investment and direct operating costs. The number of orders made by customers in the logistics section of warehouse management is very high because the number, type of products and items ordered by different customers vary broadly. However, machines layout for picking up products at logistics centres is minimal, inflexible, and, in some cases, inconclusive. In this study, we address joint order batching procedures of orders considering picker routing problem as a mixed-integer programming model. Extensive numerical experiments were generated in small, medium, and large sizes. In order to consider the uncertainty of parameters, we applied robust possibilistic programming for this problem. Three different meta-heuristic algorithms; genetic algorithm, particle swarm optimisation algorithm, and honey artificial bee colony algorithms are used as solution approaches to solve the formulated model. The performance of solution approaches over the problem was analysed using several test indexes. In all three group examples, there was no significant difference among mean values of the objective function, while there was a remarkable difference among computing times. [ABSTRACT FROM AUTHOR]

The shuttle-based storage and retrieval system (SBS/RS) is a relatively new part-to-picker order picking system. We have developed a performance estimation and design algorithm for the SBS/RS. The performance estimation is based on a queuing model. The design algorithm aims to find the minimum cost configurations in terms of number of tiers, aisles, lifts and workstations with given throughput, tote capacity and order cycle time requirements. We used simulation driven by parameters abstracted from an actual SBS/RS to verify the performance estimation, and applied the design algorithm in the case study. The results indicate that: (1) compared to simulation results, the throughput of the performance estimation is nearly identical when the arrival rate is below the maximum capacity; (2) the design algorithm yields a configuration with 28.1% cost reduction in the current system. In addition, we also compared the shuttle system with the competing robotic order fulfilment system (robotic system in short) in terms of facility cost, building cost and order cycle time. We found that the shuttle system is a better choice if large storage capacity and high throughput are required whereas the robotic order fulfilment system performs better if small storage capacity and low throughput are required. [ABSTRACT FROM AUTHOR]

In this paper, a novel approach has been proposed for hiding sensitive association rules based on the affinity between the frequent items of the transaction. The affinity between the items is defined as Jaccard similarity. This work proposes five algorithms to ensure the minimum side-effects resulting after applying sanitization algorithms to hide sensitive knowledge. Transaction affinity has been introduced which is calculated by adding the affinity of frequent items present in the transaction with the victim-item (item to be modified). Transactions are selected either by increasing or decreasing value of affinity for data distortion to hide association rules. The first two algorithms, MaxaffinityDSR and MinaffinityDSR, hide the sensitive information by selecting the victim item as the right-hand side of the sensitive association rule. The next two algorithms, MaxaffinityDSL and MinaffinityDSL, select the victim item from the left-hand side of the rule whereas the Hybrid approach picks the victim item from either the left-hand side or right-hand side. The performance of proposed algorithms has been evaluated by comparison with state-of-art methods (Algo 1.a and Algo 1.b), MinFIA, MaxFIA and Naive algorithms. The experiments were performed using the dataset generated from IBM synthetic data generator, and implementation has been performed in R language. [ABSTRACT FROM AUTHOR]

To solve the two-workshop integrated scheduling problem with the same device resources, existing algorithms pay attention to the horizontal parallel processing of the process tree and ignore the tightness between vertical serial processes. A scheduling algorithm for two-workshop production with the time-selective strategy and Backtracking Strategy is proposed. The scheduling order of each process in the process tree needs to be determined, which will be completed by the process sequence sequencing strategy. The scheduling plan also needs to be determined, which will be completed using the time-selective scheduling strategy for the two workshops. At the same time, the "reference time" is set for the current scheduling process. To find a better scheduling scheme, the "scheduling reference time" is recorded as T. If the time of the current scheduling process scheme is greater than T, the backtracking adjustment strategy will be used to track the process scheduling scheme. Finally, experiments show that the algorithm not only ensures the parallel processing of parallel processes but also effectively improves the tightness of serial processes and optimizes the results of integrated scheduling. [ABSTRACT FROM AUTHOR]

The existing multi-manipulator sorting method for gangue that utilizes a multi-task allocation strategy is not satisfactory. The single manipulator working space is fixed, lowering the cooperation degree between the manipulators and leading to a low sorting rate. Therefore, this paper proposes a multi-manipulator cooperative sorting method that can work globally. First, a benefit function based on the sorting time and quality of the gangue is constructed by combining the gangue flow information and the manipulator state. The time parameter is obtained via the manipulator's dynamic target tracking trajectory planning algorithm based on PID control. Secondly, the benefits matrix is standardized and updated many times to improve the Hungarian algorithm to achieve task allocation, and the initial solution with priority is obtained. Finally, the solutions are analyzed and processed cooperatively in order of priority. The conflicts between multiple robotic arms are eliminated through task cooperation and trajectory cooperation until the sorting task that the robot arm can execute is obtained from the allocation results. Experiments involving different sorting methods were completed on a multi-arm coal and gangue sorting experimental robot platform. The experimental results show that the sorting efficiency of the proposed method is about 10% and 20% higher than that of the fixed space dynamic and designated space fixed points methods, respectively, under different belt speeds. This method can guarantee system benefits, effectively implements cooperative control of multi-manipulator operations in the whole area, and improves the efficiency of coal gangue sorting. [ABSTRACT FROM AUTHOR]

Dual-layer displays (DLDs) can achieve high dynamic range by reducing light leakage via bonding two LC panels. However, the physical distance between the two LC panels introduces a parallax error when the display is viewed off-axis. This paper proposes a convolutional neural network-based image splitting algorithm for improving the display quality of a DLD. In order to improve processing speed, the network structure is designed relatively simply. The training dataset is established mainly based on TV signals. Different from the common image evaluation metrics based on PSNR and SSIM, the paper proposes to apply learned perceptual image patch similarity (LPIPS) to define the loss function for DLDs. The simulation and experiment results show that compared with the existing method, the proposed method has better performance in reducing parallax error and improving image details. The processing time can be shortened by 2.7 times. The proposed CNN-based method can greatly reduce the processing time while maintaining high display quality, which presents high practicability. [ABSTRACT FROM AUTHOR]

Order picking, which is collecting a set of products from different locations in a warehouse, has repeatedly been described as one of the most laborious and time-consuming internal logistic processes. Each order is issued to pick some products located at given locations in the warehouse. In this paper, we consider an order picking problem, in which a number of orders with different delivery due dates are going to be retrieved by a limited number of order pickers in multiperiods such that the total tardiness is minimized. The aim is to determine a retrieval plan in terms of order batching and order picker multitrip routing as decision variables. Besides, products are arrived and replenished at the predetermined locations at different periods. Therefore, products sitting in those locations should be delivered soon to provide empty rooms for replenishment. A mixed integer linear programming formulation is proposed for this new problem. The model is optimally solved for small-size problems. For larger instances, grouping metaheuristic algorithms are proposed based on particle swarm optimization and the league championship algorithm that use group-based operators to generate reasonable batches of orders. Improvement heuristics are designed as well. The performance of the MILP formulation and metaheuristic algorithms is analyzed for different problem instances whose designs are based on real data gathered from an auto parts warehouse. Results indicate that our algorithms can stably solve large instances of the problem in a reasonable time. [ABSTRACT FROM AUTHOR]

In order to effectively allocate processes of Aa single complex product to two workshops with the same equipment, an integrated scheduling algorithm of two workshops based on process end time driven and processing area priority is proposed. To optimize the total processing time, a process scheduling strategy based on process end time driven is proposed. By scheduling multiple devices at the same time, the parallelism of process execution is improved, in order to reduce the total processing time and improve the utilization rate of equipment. In order to optimize the process migration times between two workshops, a workshop assignment strategy based on process processing area priority is proposed, which comprehensively considers the influence of the immediate preceding process, neighbor process and friend process of the schedulable process on workshop assignment and makes the process in the same processing area be processed in the same workshop as much as possible, so as to reduce the process migration times. The example analysis shows that the proposed algorithm is superior to the existing two workshops integrated scheduling algorithms in terms of reducing the total processing time, reducing the process migration times, and improving the utilization rate of equipment. [ABSTRACT FROM AUTHOR]

In the order picking process of the warehouse center, considering the rapid increase in the volume of orders arriving at the picking center at the time of the promotional festival, a hybrid operation mode with multiple picking tables is used to meet the picking requirements of the huge number of real-time orders. Therefore, in this paper, a hybrid picking mode is proposed, taking into account both the idle degree of picking stations and their order item centers of gravity, and a new reinforcement learning algorithm embedding mechanism (PRL) with placeholder control is designed to solve the problem of a huge number of real-time item orders arriving at the picking center system on promotional holidays and in inconsistent quantities, and numerical simulations are performed for this algorithm. The experimental results show that the PRL algorithm in hybrid picking mode can handle a huge number of orders simultaneously and improve picking efficiency effectively. [ABSTRACT FROM AUTHOR]

Motivated by applications in data center networks, in this paper, we study the problem of scheduling in an input queued switch. While throughput maximizing algorithms in a switch are well-understood, delay analysis was developed only recently. It was recently shown that the well-known MaxWeight algorithm achieves optimal scaling of mean queue lengths in steady state in the heavy-traffic regime, and is within a factor less than 2 of a universal lower bound. However, MaxWeight is not used in practice because of its high time complexity. In this paper, we study several low complexity algorithms and show that their heavy-traffic performance is identical to that of MaxWeight. We first present a negative result that picking a random schedule does not have optimal heavy-traffic scaling of queue lengths even under uniform traffic. We then show that if one picks the best among two matchings or modifies a random matching even a little, using the so-called flip operation, it leads to MaxWeight like heavy-traffic performance under uniform traffic. We then focus on the case of non-uniform traffic and show that a large class of low time complexity algorithms have the same heavy-traffic performance as MaxWeight, as long as it is ensured that a MaxWeight matching is picked often enough. We also briefly discuss the performance of these algorithms in the large scale heavy-traffic regime when the size of the switch increases simultaneously with the load. Finally, we perform empirical study on a new algorithm to compare its performance with some existing algorithms. [ABSTRACT FROM AUTHOR]

In the communication of RFID system, it is easy to cause collision during data exchange between the tag and the reader, thus tags may not be identified. On the basis of researching binary algorithm, bit positions of collision are locked through setting different statuses of the order during collision, and then samples of the locked positions are recorded to select the remaining collision positions during the paging process to the descending order. Compared with traditional binary collision algorithm, significant improvements have been made in analyzing the algorithm's indexes like searching times, amount of communication and throughput, improving efficiency of the system. [ABSTRACT FROM AUTHOR]

Downward-looking linear array synthetic aperture radar (DLLA SAR) is a kind of three-dimensional (3-D) radar imaging system. To obtain the superresolution along the crosstrack direction of DLLA SAR, the sparse regularization models with single measurement vector (SMV) have been widely applied. However, the robustness of the sparse regularization models with SMV is unsatisfactory, especially in the low signal-to-noise rate (SNR) environment. To solve this problem, we proposed a novel imaging method for DLLA SAR based on the multiple measurement vector (MMV) model with L 2 , 1 -norm. At first, we exchange the processing order between the along-track (AT) domain and the crosstrack (CT) domain to keep the same sparse structure of the signal in the crosstrack domain so that we can establish the imaging problem as a sparse regularization model based on the MMV model. Moreover, the mixed L 2 , 1 -norm is introduced into the regularization term of the MMV model. Finally, the modified orthogonal matching pursuit (OMP) algorithm is designed for the MMV model with the L 2 , 1 -norm. The simulations verify that the proposed method has better performance in the lower SNR environment and requires lower computation compared with the conventional methods. [ABSTRACT FROM AUTHOR]

The constrained competitive location problem is a classic NP-hard problem in combinatorial optimization. When the existing algorithms study this problem, the optimal solution cannot be obtained or the solution speed is slow. Aiming at the shortcomings of the existing algorithms, this paper modified this classic problem and constructed a new mathematical model. Then it studied the mathematical properties of the model, and proposed upper and lower bound algorithms and lower bound algorithms on the basis of mathematical properties. The order sub-algorithm reduced the order of the problem and achieved the purpose of reducing the search solution space of the problem. There were both single order reduction and batch reduction in the order reduction process. Then it designed a backtracking sub-algorithm on the basis of the previous one to solve the optimal solution of the problem. Finally, the principle of the algorithm was explained more clearly through the analysis of two examples, and the result proves that the algorithm can find the optimal solution faster. [ABSTRACT FROM AUTHOR]

Copyright of Chinese Journal of Liquid Crystal & Displays is the property of Chinese Journal of Liquid Crystal & Displays and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

• The dynamics of cross-diffusion leads to a high complexity on implicit finite difference schemes implementations. • Two stable operator splitting schemes are proposed to lower the complexity. • A stable factorization of the system matrix is derived to fasten the computations. • We achieve competitive running times (similar to explicit approaches) for these implicit implementations. The dynamics of cross-diffusion models leads to a high computational complexity for implicit difference schemes, turning them unsuitable for tasks that require results in real-time. We propose the use of two operator splitting schemes for nonlinear cross-diffusion processes in order to lower the computational load, and establish their stability properties using discrete L 2 energy methods. Furthermore, by attaining a stable factorization of the system matrix as a forward-backward pass, corresponding to the Thomas algorithm for self-diffusion processes, we show that the use of implicit cross-diffusion can be competitive in terms of execution time, widening the range of viable cross-diffusion coefficients for on-the-fly applications. [ABSTRACT FROM AUTHOR]

With the advancement of AI and the widespread application of IoT and cloud computing, the pick-and-pass system (PKPS) can potentially be transformed into a cyber-physical system (CPS) based intelligent warehouse picking system. This paper proposes a CPS-based PKPS with a heuristic multi-objective genetic algorithm to solve the NP-hard storage assignment problem (SAP) for order picking operations in an e-commerce-based warehouse. The proposed algorithm considers both the workload balance between picking lines and emergency replenishment during picking operation. Finally, the study shows that the proposed algorithm is effective in improving the efficiency of picking operations based on software simulation. [ABSTRACT FROM AUTHOR]

This article investigates the cooperative task planning problem of heterogeneous multi-robot systems in an order picking warehouse, with challenging characteristics such as heterogeneous agents, interrelated utilities and complex intertask dependencies. These characteristics bring great challenges for modelling and optimization of the problem. This study establishes a novel mapping mechanism to describe the problem as open shop scheduling with sequence-dependent set-up and transportation times, and develops an efficient mixed-integer linear programming model to obtain the optimal task planning scheme for small-size problems. To increase the size of solvable problems and improve the performance of the solution, an effective hybrid artificial bee colony algorithm is proposed, which introduces some concepts from the tabu search algorithm and the artificial immune algorithm. The effectiveness of the proposed method is confirmed by simulation experiments. [ABSTRACT FROM AUTHOR]

In modern conditions, due to the vastness of the territory of Kazakhstan, with a certain probability, natural disasters such as earthquakes, floods, avalanches, as well as accidents, destruction of buildings, epidemics, release of chemical toxic substances at industrial enterprises, fires in educational and medical institutions are possible, which justifies the relevance of modern methods and technologies for solving the problem of evacuation. The peculiarity of this work lies in the formation of an integrated approach for organizing the evacuation process both in peacetime as training for the event of an emergency situation (emergency), and in the event of the emergency itself. A conceptual diagram of an evacuation system is proposed that uses heterogeneous sources for receiving and transmitting information about the onset of an emergency. The input and output sources for receiving and transmitting information about the number of people in the building are determined. The main purpose of the system is to form an operational real-time evacuation plan. This work is the result of a phased implementation of an integrated evacuation system, which consists in building a mathematical model and a method for solving the problem of maximum flow in the network. A mathematical model has been developed for the optimal flow distribution along the Grindshiels network with the analysis of the flow formation and the characteristics of people’s motion in enclosed spaces. A game-theoretic approach and mathematical methods of the theory of hydraulic networks for finding an equilibrium state in flow-distribution networks have been developed. An algorithm for solving the evacuation problem using the graph approach is proposed. The results of this paper make it possible to systematically organize training evacuations, prepare resources, train the personnel responsible for evacuation in order to quickly respond in an emergency and carry out the evacuation process in order to avoid major consequences. [ABSTRACT FROM AUTHOR]

Copyright of Control Theory & Applications / Kongzhi Lilun Yu Yinyong is the property of Editorial Department of Control Theory & Applications and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

In many applications of modern quality control, process monitoring involves a large number of process variables and quality characteristics. Practitioners are desired to attain complete information about the process in order to assure quick detection of shifts that may possibly occur at any variable. However, full information is not always available during online monitoring of big data streams due to limitations of monitoring resources in practice. In this paper, a rank-based monitoring and sampling algorithm based on data augmentation is proposed to quickly detect the mean shifts in a process when only a limited portion of observations are available online. Specifically, at each observation time, the proposed method will automatically augment information for unobservable variables based on the online observations, and then intelligently allocate the monitoring resources to the most suspicious data streams. Comparing to the existing literature, this method is able to accurately infer the status of all variables in a process based on a small number of observable variables and effectively construct a global monitoring statistic with the proposed augmented vector, which leads to a quick detection of the out-of-control status even if limited shifted variables are observed in real time. Simulation studies as well as a real case study on real-time solar flare detection are conducted to demonstrate the efficacy and applicability of the proposed method. [ABSTRACT FROM AUTHOR]

In this paper, we define s-nonstationary iterative process and obtain its properties. We prove, that for any one-point iterative process without memory, there exists an s-nonstationary process of the same order, but of higher efficiency by the criteria of Traub and Ostrowski. We supply constructions of s-nonstationary processes for Newton's, Halley's, and Chebyshev's methods, obtain their properties and, for some of them, also their geometric interpretation. The algorithms we present can be transformed into computer programs in a straightforward manner. Additionally, we illustrate numerical examples, as demonstrations for the methods we present. [ABSTRACT FROM AUTHOR]

Imbalanced process arrival patterns (PAPs) are ubiquitous in many parallel and distributed systems, especially in HPC ones. The collective operations, e.g. in MPI, are designed for equal process arrival times, and are not optimized for deviations in their appearance. We propose eight new PAP-aware algorithms for the scatter and gather operations. They are binomial or linear tree adaptations introducing additional process ordering and (in some cases) additional activities in a special background thread. The solution was implemented using one of the most popular open source MPI compliant library (OpenMPI), and evaluated in a typical HPC environment using a specially developed benchmark as well as a real application: FFT. The experimental results show a significant advantage of the proposed approach over the default OpenMPI implementation, showing good scalability and high performance with the FFT acceleration for the communication run time: 16.7% and for the total application execution time: 3.3%. [ABSTRACT FROM AUTHOR]

In this paper we introduce some new definitions for sequences of operations and extract general theorems about properties of iterative algorithms encoded in nominative data language [20] in the Mizar system [3], [1] in order to simplify the process of proving algorithms in the future. This paper continues verification of algorithms [10], [13], [12], [14] written in terms of simple-named complex-valued nominative data [6], [8], [18], [11], [15], [16]. The validity of the algorithm is presented in terms of semantic Floyd-Hoare triples over such data [9]. Proofs of the correctness are based on an inference system for an extended Floyd-Hoare logic [2], [4] with partial pre- and postconditions [17], [19], [7], [5]. [ABSTRACT FROM AUTHOR]

A reflex is a simple closed-loop control approach that tries to minimize an error but fails to do so because it will always react too late. An adaptive algorithm can use this error to learn a forward model with the help of predictive cues. For example, a driver learns to improve steering by looking ahead to avoid steering in the last minute. In order to process complex cues such as the road ahead, deep learning is a natural choice. However, this is usually achieved only indirectly by employing deep reinforcement learning having a discrete state space. Here, we show how this can be directly achieved by embedding deep learning into a closed-loop system and preserving its continuous processing. We show in z-space specifically how error backpropagation can be achieved and in general how gradient-based approaches can be analyzed in such closed-loop scenarios. The performance of this learning paradigm is demonstrated using a line follower in simulation and on a real robot that shows very fast and continuous learning. [ABSTRACT FROM AUTHOR]

This paper is in view of the current algorithm to solve Multiple-Devices-Process integrated scheduling problems without considering parallel process between the parallel processing. Meanwhile, the influence of the first processing procedure on the second processing procedure is ignored, resulting in poor tightness between serial processes and poor parallelism between parallel processes, ultimately affecting the product scheduling results. We proposed Multiple-Devices-Process integrated scheduling algorithm with time-selective for process sequence. The proposed Multiple-Devices-Process sequencing strategy determines the process of scheduling order and improves the tightness between serial process. This paper presents a method to determine the quasischeduling time point of the multiequipment process, the time-selective strategy of Multiple-Devices-Process, and the time-selective adjustment strategy of Multiple-Devices-Process so that the first and the second processing processes cooperate with each other, and the purpose of improving the tightness of the serial process and the parallelism of the parallel processes is achieved, so as to shorten the product processing time. [ABSTRACT FROM AUTHOR]

A robotic mobile fulfillment system (RMFS) performs the order fulfillment process by bringing inventory to workers at pick-pack-and-ship warehouses. In the RMFS, robots lift and carry shelving units, called inventory pods, from storage locations to picking stations where workers pick items off the pods and put them into shipping cartons. The robots then return the pods to the storage area and transport other pods. In this article, we consider an item assignment problem in the RMFS in order to maximize the sum of similarity values of items in each pod. We especially focus on a reoptimization heuristic to address the situation where the similarity values are altered so that a good assignment solution can be obtained quickly with the changed similarity values. A constructive heuristic algorithm for the item assignment problem is developed, and then, a reoptimization heuristic is proposed based on the constructive heuristic algorithm. Then, computational results for several instances of the problem with 10–500 items are presented. We further analyze the case for which an item type can be placed into two pods. Note to Practitioners—This article proposes an efficient heuristic algorithm for assigning items to pods in a robotic mobile fulfillment system (RMFS) so that items ordered together frequently are put into the same pod. Computational results with 10–500 items show that the gaps from upper bounds are very small on average. For cases where the similarity values between items change or their estimation is not accurate due to the fluctuations in demand, a reoptimization heuristic algorithm that alters the original assignment is developed. The experimental results show that the reoptimization algorithm is robust when perturbation levels are approximately 40%–50% of the original similarity values with much less computation times. We believe that this research work can be very helpful for operating the RMFS efficiently. [ABSTRACT FROM AUTHOR]

This paper proposes a modification approach for phased-based EVM in order to reduce the processing time without effect the quality of the magnified video. The proposed approach applies a resizing process on the input video using Lanczos-3 algorithm. Then, it decomposes video frames using steerable pyramid to obtain multi-scale frame with its orientation. Subsequently, the resulted frames are filtered by temporal filters for specific bands and the filtered frames are multiplied by a magnification factor. Now, both the magnified regions and the unmagnified regions for each frame are added together. Finally, reconstructing the produced magnified multi-scale frames using the inverse steerable pyramid. The experimental results show that superiority of the proposed approach compares to the conventional phase-based EVM in processing time, where the processing time reduction about 60-65%. Furthermore, this approach does not affect on the video quality, which maintain it in the boundary of the conventional Phase-based EVM. [ABSTRACT FROM AUTHOR]

Existing Adaptive Bitrate (ABR) algorithms pick future video chunks’ bitrates via fixed rules or offline trained models to ensure good quality of experience (QoE) for Internet video. Nevertheless, data analysis demonstrates that a good ABR algorithm is required to continually and fast update for adapting itself to time-varying network conditions. Therefore, we propose Comyco, a video quality-aware learning-based ABR approach that enormously improves recent schemes by i) picking the chunk with higher perceptual video qualities rather than video bitrates; ii) training the policy via imitating expert trajectories given by the expert strategy; iii) employing the lifelong learning method to continually train the model w.r.t the fresh trace collected by the users. To achieve this, we develop a complete quality-aware lifelong imitation learning-based ABR system, construct quality-based neural network architecture, collect a quality-driven video dataset, and estimate QoE metrics with video quality features. Using trace-driven and real-world experiments, we demonstrate Comyco reaches 1700-fold improvements in the number of samples required and 16-fold speedup in the training time compared with the prior work. Meanwhile, Comyco outperforms existing methods, with the improvements on average QoE of 7.5%–16.79%. Moreover, experimental results on continual training also illustrate that lifelong learning helps Comyco further improve the average QoE of 1.07%–9.81% in comparison to the offline trained model. [ABSTRACT FROM AUTHOR]

In this paper, a brief working principle of synthetic aperture radar (SAR) will be covered. The details of the range Doppler algorithm (RDA) will be studied including the mathematical models of the involved transmitted and received signals as well as the algorithm. The implementation and algorithm development of the RDA will be carried out in MATLAB. The processing steps of the RDA will be illustrated in detail together with the SAR raw signal simulation as point targets and its mathematical models. Several SAR signal properties will be highlighted as these parameters are relatively important in order to develop the algorithm for focusing SAR data. Analytical expressions of baseband SAR signal that consist of point targets are derived and they are used to generate the SAR raw data in MATLAB for further SAR processing. The derivation of the expressions allows the design of the matched filters that are used in range compression and azimuth compression in the RDA in order to process the SAR raw data. The SAR raw data will be processed by using the developed RDA and will be illustrated in the form of SAR images. [ABSTRACT FROM AUTHOR]

The paper proposes an algorithm based on the Multi-State Constraint Kalman Filter (MSCKF) algorithm to construct the map for robots special in the poor GPS signal environment. We can calculate the position of the robots with the data collected by inertial measurement unit and the features extracted by the camera with MSCKF algorithm in a tight couple way. The paper focuses on the way of optimizing the position because we adopt it to compute Kalman gain for updating the state of robots. In order to reduce the processing time, we design a novel fast Gauss–Newton MSCKF algorithm to complete the nonlinear optimization. Compared with the performance of conventional MSCKF algorithm, the novel fast-location algorithm can reduce the processing time with the kitti datasets. [ABSTRACT FROM AUTHOR]

This paper presents a low cost hardware message passing engine of enhanced atomic pipelined broadcast based on processing node status. In this algorithm, the previous atomic pipelined broadcast algorithm is modified to reduce the waiting time until next broadcast communication. For this, the processor change the transmission order of processing nodes based on the nodes’ communication channel. Also, the hardware message passing engine architecture of the proposed algorithm is modified to be adopted to multi-core processor. The synthesized logic area of the proposed hardware message passing engine was reduced by about 16%, compared by the pre-existing hardware message passing engine. [ABSTRACT FROM AUTHOR]

Two‐dimensional digital image correlation (2D‐DIC) is an experimental technique used to measure in‐plane displacement of a test specimen. Real‐time measurement of full‐field displacement data is challenging due to enormous computational load of the algorithm. In order to improve the computational speed, the focus of recent research works has been on the approach of parallelization across subsets within image pairs using graphics processing unit (GPU). But alternate GPU‐based parallelization approaches to improve the performance of this algorithm as per the order of data processing have not been explored. To address this research gap, our method utilizes parallelism within a subset as well as across subsets for each computation step in an iteration cycle. A heterogeneous (CPU‐GPU) framework in combination with a pyramid‐based initial values estimation for subsets (in parallel) is proposed in this work. The precompute steps of the proposed framework are implemented using CPU, whereas the main iterative steps are realized using GPU. It is demonstrated that the overall computational speed of the proposed heterogeneous framework improves by ≈9× compared to a sequential CPU‐based implementation for a pair of gray‐scale images with a resolution of 588×2,048 pixels. As an important milestone, feasibility to measure deformations in real time (≤ 1 s) is manifested in this study. [ABSTRACT FROM AUTHOR]

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The demand for application of mobile robots in performing boring and extensive tasks are increasing rapidly due to unavailability of human workforce. Navigation by humans within the warehouse is one among such repetitive and exhaustive task. Autonomous navigation of mobile robots for picking and dropping the shelves within the warehouse will save time and money for the warehousing business. Proposing an optimization model for automated storage and retrieval systems by the goals of its planning is investigated to minimize travel time in multi-robot systems. This paper deals with designing a system for storing and retrieving a group of materials within an environment arranged in rows and columns. Its intersections represent storage locations. The title of any subject is indicated by the row number and the column in it. A method was proposed to store and retrieve a set of requests (materials) using a number of robots as well as one receiving and delivery port. Several simulation results are tested to show this improvement in length of path and time of arrival. [ABSTRACT FROM AUTHOR]

One of the main points when implementing the Information Technology Infrastructure Library (ITIL) is which order the processes must be implemented. In the systematic literature review (SLR) developed, it is possible to find references about strategies and factors that ease the implementation of the ITIL, static sequences for the processes to be implemented, and recommendations about the first process to implement, but it is rather complicated to find references that explicitly define the order (adapted to a specific company) of the processes to be implemented. Thus, once it is shown that there is no methodology/algorithm providing a sequence of ITIL processes specifically adapted for each company, an algorithm to solve this problem is presented: The algorithm has a deep mathematical basis and returns a sequence of ITIL processes to optimize the efforts during implementation, so the company implementing the ITIL gets the closest to the competitors. The optimization is made considering parameters such as staff, age of the company, IT size, industry, etc. Thus, the sequence proposed is specific for each company. Finally, a comparative of the sequence obtained (from the proposed algorithm) with sequences discovered in the SLR is presented and applied to a real case. [ABSTRACT FROM AUTHOR]

In this study, we propose a model to minimize the inventory and location costs of a supply chain, including a production plant, warehouses and retailers. The production plant distributes a single product to retailers through warehouses. The model determines the location of warehouses, allocates retailers to the warehouses and indicates the length of order intervals at warehouses and retailers. To ensure the order quantity is lower than the warehouses' capacity, we consider the capacity constraints. Unlike the existing researches, we investigate the limitation on the number of established warehouses. We formulize the problem as a nonlinear mixed-integer model and propose two efficient meta-heuristic algorithms including a genetic algorithm (GA) and an evolutionary simulated annealing algorithm (ESA) to solve it. To improve the proposed algorithms, in generating populations, a new heuristic method which produces feasible solutions is designed. The Taquchi method is used for tuning the parameters of the proposed algorithms. The small size numerical examples are solved and sensitivity analysis is done to demonstrate the influential theoretical results. We evaluate the proposed algorithms by comparing the solutions of them with the optimal solution obtained from the Lingo 11. Further, we investigate their efficiency by solving numerical examples in different sizes and depict the percentage gaps between the best solution values and the average objective values of them. Results reveal that both the GA and ESA are efficient in solving the proposed model, but the ESA outperforms the GA on the optimal solution, computing time and stability. [ABSTRACT FROM AUTHOR]

This paper addresses the order picking sequence problem in an automated warehouse where parts of an order picker's travelling tour are fixed, the length of the layout is significantly longer than the width, and the picker can hold only one item at a given time. The problem is to find an optimal sequence with given vertical paths and so it is a special type of travelling salesman problem. A simple sorting-based heuristic and an efficient clustering-based algorithm are developed for this problem. In order to use the gantry robots more efficiently, a flexible drop buffer assignment is considered and the proposed algorithms are modified for the new operating condition. Experimental results indicate the efficiency of the proposed algorithms. [ABSTRACT FROM AUTHOR]

Improving the overall performance of warehouse operation is the goal of every operator be it in the manufacturing or in a distribution business setting. To do this, an efficient optimization of order picking process is needed to reduce the time for an orders to ship out, which in turn, will improve the overall effectiveness of its workers. Several approaches had been applied including the improvement of warehouse layout, routing methods, item storage set-up and batching. This paper focused on the routing method to improve the order picking process and selected different studies from 2013 to 2018 for analyzation and evaluation. The selection of different studies was based on the method they applied such as the used of unmodified algorithm, enhancement or modification of existing algorithm, combining several algorithm, and other framework or model to improve the routing methods. Results shown that the unmodified algorithm had limited features and capability. Modification greatly improve the efficiency and effectiveness in its application and combining 2 or more algorithm resulted better results and efficiency and effectiveness. Lastly, the application of other frameworks were also useful to improve order picking time but the consistency in its application requires constant analysis, and evaluation to measure OP time performance. [ABSTRACT FROM AUTHOR]